WO2013184070A8 - A drusen lesion image detection system - Google Patents

A drusen lesion image detection system Download PDF

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Publication number
WO2013184070A8
WO2013184070A8 PCT/SG2013/000235 SG2013000235W WO2013184070A8 WO 2013184070 A8 WO2013184070 A8 WO 2013184070A8 SG 2013000235 W SG2013000235 W SG 2013000235W WO 2013184070 A8 WO2013184070 A8 WO 2013184070A8
Authority
WO
WIPO (PCT)
Prior art keywords
drusen
transformed data
local descriptor
image
detection system
Prior art date
Application number
PCT/SG2013/000235
Other languages
French (fr)
Other versions
WO2013184070A1 (en
Inventor
Wing Kee Damon Wong
Xiangang Cheng
Jiang Liu
Ngan Meng TANG
Beng Hai Lee
Fengshou Yin
Mayuri BHARGAVA
Gemmy CHEUNG
Tien Yin Wong
Original Assignee
Agency For Science, Technology And Research
Singapore Health Services Pte Ltd
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Agency For Science, Technology And Research, Singapore Health Services Pte Ltd filed Critical Agency For Science, Technology And Research
Priority to US14/406,201 priority Critical patent/US20150125052A1/en
Priority to SG11201407700RA priority patent/SG11201407700RA/en
Publication of WO2013184070A1 publication Critical patent/WO2013184070A1/en
Publication of WO2013184070A8 publication Critical patent/WO2013184070A8/en

Links

Classifications

    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G06T7/0002—Inspection of images, e.g. flaw detection
    • G06T7/0012—Biomedical image inspection
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F18/00—Pattern recognition
    • G06F18/20—Analysing
    • G06F18/23—Clustering techniques
    • G06F18/232—Non-hierarchical techniques
    • G06F18/2321—Non-hierarchical techniques using statistics or function optimisation, e.g. modelling of probability density functions
    • G06F18/23213—Non-hierarchical techniques using statistics or function optimisation, e.g. modelling of probability density functions with fixed number of clusters, e.g. K-means clustering
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F18/00—Pattern recognition
    • G06F18/20—Analysing
    • G06F18/285—Selection of pattern recognition techniques, e.g. of classifiers in a multi-classifier system
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G06T7/40—Analysis of texture
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G06T7/90—Determination of colour characteristics
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V10/00—Arrangements for image or video recognition or understanding
    • G06V10/40—Extraction of image or video features
    • G06V10/50—Extraction of image or video features by performing operations within image blocks; by using histograms, e.g. histogram of oriented gradients [HoG]; by summing image-intensity values; Projection analysis
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V10/00—Arrangements for image or video recognition or understanding
    • G06V10/70—Arrangements for image or video recognition or understanding using pattern recognition or machine learning
    • G06V10/762—Arrangements for image or video recognition or understanding using pattern recognition or machine learning using clustering, e.g. of similar faces in social networks
    • G06V10/763—Non-hierarchical techniques, e.g. based on statistics of modelling distributions
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V10/00—Arrangements for image or video recognition or understanding
    • G06V10/70—Arrangements for image or video recognition or understanding using pattern recognition or machine learning
    • G06V10/87—Arrangements for image or video recognition or understanding using pattern recognition or machine learning using selection of the recognition techniques, e.g. of a classifier in a multiple classifier system
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V40/00—Recognition of biometric, human-related or animal-related patterns in image or video data
    • G06V40/10—Human or animal bodies, e.g. vehicle occupants or pedestrians; Body parts, e.g. hands
    • G06V40/18—Eye characteristics, e.g. of the iris
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/30—Subject of image; Context of image processing
    • G06T2207/30004—Biomedical image processing
    • G06T2207/30041—Eye; Retina; Ophthalmic

Abstract

A method is proposed for automatically analysing a retina image, to identify the presence of drusen which is indicative of age-related macular degeneration. The method proposes dividing a region of interest including the macula centre into patches, obtaining a local descriptor of each of the patches, reducing the dimensionality of the local descriptor by comparing the local descriptor to a tree-like clustering model and obtaining transformed data indicating the identity of the cluster. The transformed data is fed into an adaptive model which generates data indicative of the presence of drusen in the retinal image. Furthermore, the transformed data can be used to obtain the location of the drusen within the image.
PCT/SG2013/000235 2012-06-05 2013-06-05 A drusen lesion image detection system WO2013184070A1 (en)

Priority Applications (2)

Application Number Priority Date Filing Date Title
US14/406,201 US20150125052A1 (en) 2012-06-05 2013-06-05 Drusen lesion image detection system
SG11201407700RA SG11201407700RA (en) 2012-06-05 2013-06-05 A drusen lesion image detection system

Applications Claiming Priority (2)

Application Number Priority Date Filing Date Title
SG201204125-7 2012-06-05
SG201204125 2012-06-05

Publications (2)

Publication Number Publication Date
WO2013184070A1 WO2013184070A1 (en) 2013-12-12
WO2013184070A8 true WO2013184070A8 (en) 2014-12-11

Family

ID=49712344

Family Applications (1)

Application Number Title Priority Date Filing Date
PCT/SG2013/000235 WO2013184070A1 (en) 2012-06-05 2013-06-05 A drusen lesion image detection system

Country Status (2)

Country Link
US (1) US20150125052A1 (en)
WO (1) WO2013184070A1 (en)

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EP3186779A4 (en) * 2014-08-25 2018-04-04 Agency For Science, Technology And Research (A*star) Methods and systems for assessing retinal images, and obtaining information from retinal images
US9773325B2 (en) * 2015-04-02 2017-09-26 Toshiba Medical Systems Corporation Medical imaging data processing apparatus and method
EP3136289A1 (en) * 2015-08-28 2017-03-01 Thomson Licensing Method and device for classifying an object of an image and corresponding computer program product and computer-readable medium
WO2017046378A1 (en) * 2015-09-16 2017-03-23 INSERM (Institut National de la Recherche Médicale) Method and computer program product for characterizing a retina of a patient from an examination record comprising at least one image of at least a part of the retina
IL245879B (en) * 2016-05-26 2021-05-31 Manela Israel System and method for use in diagnostics of eye condition
JP6662246B2 (en) * 2016-09-01 2020-03-11 カシオ計算機株式会社 Diagnosis support device, image processing method in diagnosis support device, and program
JP6702118B2 (en) * 2016-09-26 2020-05-27 カシオ計算機株式会社 Diagnosis support device, image processing method in the diagnosis support device, and program
US11205103B2 (en) 2016-12-09 2021-12-21 The Research Foundation for the State University Semisupervised autoencoder for sentiment analysis
CN107358606B (en) * 2017-05-04 2018-07-27 深圳硅基仿生科技有限公司 The artificial neural network device and system and device of diabetic retinopathy for identification
CN108416344B (en) * 2017-12-28 2021-09-21 中山大学中山眼科中心 Method for locating and identifying eyeground color optic disk and yellow spot
CN109816637B (en) * 2019-01-02 2023-03-07 电子科技大学 Method for detecting hard exudation area in fundus image
CN109859172A (en) * 2019-01-08 2019-06-07 浙江大学 Based on the sugared net lesion of eyeground contrastographic picture deep learning without perfusion area recognition methods
CN112419253B (en) * 2020-11-16 2024-04-19 中山大学 Digital pathology image analysis method, system, equipment and storage medium

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DE4228476C2 (en) * 1992-08-27 2002-05-02 Cognis Deutschland Gmbh Process for the recovery of tocopherol and / or sterol
JP2003126043A (en) * 2001-10-22 2003-05-07 Canon Inc Ophthalmologic photographic apparatus
US7668351B1 (en) * 2003-01-17 2010-02-23 Kestrel Corporation System and method for automation of morphological segmentation of bio-images
US7218796B2 (en) * 2003-04-30 2007-05-15 Microsoft Corporation Patch-based video super-resolution
US7248736B2 (en) * 2004-04-19 2007-07-24 The Trustees Of Columbia University In The City Of New York Enhancing images superimposed on uneven or partially obscured background
US7949186B2 (en) * 2006-03-15 2011-05-24 Massachusetts Institute Of Technology Pyramid match kernel and related techniques
US20100142767A1 (en) * 2008-12-04 2010-06-10 Alan Duncan Fleming Image Analysis
US8896682B2 (en) * 2008-12-19 2014-11-25 The Johns Hopkins University System and method for automated detection of age related macular degeneration and other retinal abnormalities
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WO2012078636A1 (en) * 2010-12-07 2012-06-14 University Of Iowa Research Foundation Optimal, user-friendly, object background separation

Also Published As

Publication number Publication date
WO2013184070A1 (en) 2013-12-12
US20150125052A1 (en) 2015-05-07

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